Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

An Analysis of the Radiometric Quality of Small Unmanned Aircraft System Imagery

In recent years, significant advancements have been made in both sensor technology and small Unmanned Aircraft Systems (sUAS). Improved sensor technology has provided users with cheaper, lighter, and higher resolution imaging tools, while new sUAS platforms have become cheaper, more stable and easier to navigate both manually and programmatically. These enhancements have provided remote sensing solutions for both commercial and research applications that were previously unachievable. However, this has provided non-scientific practitioners with access to technology and techniques previously only available to remote sensing professionals, sometimes leading to improper diagnoses and results. The work accomplished in this dissertation demonstrates the impact of proper calibration and reflectance correction on the radiometric quality of sUAS imagery. The first part of this research conducts an in-depth investigation into a proposed technique for radiance-to-reflectance conversion. Previous techniques utilized reflectance conversion panels in-scene, which, while providing accurate results, required extensive time in the field to position the panels as well as measure them. We have positioned sensors on board the sUAS to record the downwelling irradiance which then can be used to produce reflectance imagery without the use of these reflectance conversion panels. The second part of this research characterizes and calibrates a MicaSense RedEdge-3, a multispectral imaging sensor. This particular sensor comes pre-loaded with metadata values, which are never recalibrated, for dark level bias, vignette and row-gradient correction and radiometric calibration. This characterization and calibration studies were accomplished to demonstrate the importance of recalibration of any sensors over a period of time. In addition, an error propagation was performed to detect the highest contributors of error in the production of radiance and reflectance imagery. Finally, a study of the inherent reflectance variability of vegetation was performed. In other words, this study attempts to determine how accurate the digital count to radiance calibration and the radiance to reflectance conversion has to be. Can we lower our accuracy standards for radiance and reflectance imagery, because the target itself is too variable to measure? For this study, six Coneflower plants were analyzed, as a surrogate for other cash crops, under different illumination conditions, at different times of the day, and at different ground sample distances (GSDs)

Radiometric calibration, validation and correction of multispectral photogrammetric imagery

Osajulkaisut: Publication 1: Lauri Markelin, Eija Honkavaara, Jouni Peltoniemi, Eero Ahokas, Risto Kuittinen, Juha Hyyppä, Juha Suomalainen, and Antero Kukko. 2008. Radiometric calibration and characterization of large-format digital photogrammetric sensors in a test field. Photogrammetric Engineering and Remote Sensing, volume 74, number 12, pages 1487-1500. © 2008 American Society for Photogrammetry and Remote Sensing (ASPRS). Publication 2: Lauri Markelin, Eija Honkavaara, Teemu Hakala, Juha Suomalainen, and Jouni Peltoniemi. 2010. Radiometric stability assessment of an airborne photogrammetric sensor in a test field. ISPRS Journal of Photogrammetry and Remote Sensing, volume 65, number 4, pages 409-421. doi:10.1016/j.isprsjprs.2010.05.003. © 2010 International Society for Photogrammetry and Remote Sensing (ISPRS). Publication 3: Eija Honkavaara, Lauri Markelin, Tomi Rosnell, and Kimmo Nurminen. 2012. Influence of solar elevation in radiometric and geometric performance of multispectral photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing, volume 67, pages 13-26. doi:10.1016/j.isprsjprs.2011.10.001. © 2011 International Society for Photogrammetry and Remote Sensing (ISPRS). Publication 4: L. Markelin, E. Honkavaara, U. Beisl, and I. Korpela. 2010. Validation of the radiometric processing chain of the Leica ADS40 airborne photogrammetric sensor. In: Wolfgang Wagner and Balázs Székely (editors). 100 Years ISPRS, Advancing Remote Sensing Science. ISPRS Technical Commission VII Symposium. Vienna, Austria. 5-7 July 2010. International Society for Photogrammetry and Remote Sensing. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume 38, part 7A, pages 145-150. ISSN 1682-1777. © 2010 by authors. Publication 5: Lauri Markelin, Eija Honkavaara, Daniel Schläpfer, Stéphane Bovet, and Ilkka Korpela. 2012. Assessment of radiometric correction methods for ADS40 imagery. Photogrammetrie - Fernerkundung - Geoinformation (PFG), volume 2012, number 3, pages 251-266. doi:10.1127/1432-8364/2012/0115Vast amounts of remote sensing data are acquire daily all over the globe from satellites, from manned or unmanned airborne platforms, and from the ground. Airborne photogrammetry provides a flexible method for acquiring high-resolution imagery in a timely manner over large areas. Aerial images are increasingly being used in a more automatic and quantitative way for applications such as land cover classification and environmental monitoring. Apart from the high geometric quality of photogrammetric sensors, also their radiometric properties are important. Different objects reflect solar irradiance according to their individual spectral and directional properties, and radiometric analysis can be used to identify such objects and changes in them. The perquisite for quantitative radiometry is the absolute radiometric calibration of the sensor, which links the recorded digital numbers to physical units. The major benefit of a radiometrically calibrated sensor is the possibility to radiometrically correct images form atmospheric effects to surface reflectance. Radiometric correction becomes a necessity, when imagery from different dates and sensors are used for quantitative image analysis. The objectives of this study were, first, to develop a vicarious method for the radiometric calibration and validation (Cal/Val) of a photogrammetric sensor in a test field. Second, three radiometric correction methods suitable for reflectance image product generation from photogrammetric images were evaluated. Finally, the influence of the solar elevation angle in the radiometric performance of multispectral photogrammetry was evaluated. The Cal/Val method developed in this study utilizes field measured nadir reflectance factors of the reference targets to match the reflectance factors measured at a laboratory in an exact imaging geometry to the current weather conditions. When evaluating the radiometric correction methods, a reflectance accuracy level of 5 % was achievable with all of the evaluated methods when using well-defined isotropic reference targets. For other targets, reflectance accuracies of between 5 and 20 % were possible. The results showed that a low solar elevation of 25° did not cause the general performance of the photogrammetric processes and 3D point cloud generation to deteriorate. The radiometric Cal/Val method presented in this study presents a step towards developing traceable processes for photogrammetric sensors. The results also confirmed the high radiometric quality of photogrammetric sensors and proved the suitability of the photogrammetric imagery for radiometric correction. This makes possible the rigorous radiometric processing of photogrammetric images and improves the quality and accuracy of automatic image interpretation and classification tasks.Kaukokartoitusdataa kerätään päivittäin suuria määriä ympäri maailmaa satelliiteista, miehitetyistä ja miehittämättömistä lentokoneista sekä maasta käsin. Fotogrammetrinen ilmakuvaus on erinomainen tapa kerätä tarkkoja kuvia haluttuna ajankohtana suuriltakin alueilta. Ilmakuvia käytetään yhä enemmän automaattisissa ja kvantitatiivisissa sovelluksissa kuten maan pinnan luokittelussa ja ympäristön seurannassa. Laadukkaiden geometristen ominaisuuksien lisäksi olennaista fotogrammetrisissa sensoreissa on niiden radiometriset ominaisuudet. Koska kohteet heijastavat auringon säteilyä yksilöllisesti aallonpituudesta ja havaintogeometriasta riippuen, voidaan radiometrisiä ominaisuuksia hyödyntää kohteiden tunnistamisessa ja muutosten seurannassa. Kvantitatiivisen radiometrian perusvaatimus on radiometrialtaan absoluuttisesti kalibroitu sensori. Radiometrisen kalibroinnin avulla sensorin tallentamat sävyarvot voidaan muuntaa fysikaalisiksi suureiksi. Kalibroidun sensorin kuvilla näkyvät ilmakehän aiheuttamat häiriöt voidaan korjata ja kuvat muuntaa vastaamaan maanpinnan heijastusta radiometrisillä korjausmenetelmillä. Radiometrinen korjaus on välttämätöntä, kun halutaan käyttää eri ajankohtina ja eri sensoreilla kerättyjä kuva-aineistoja kvantitatiivisessa analyysissä. Tämän työn tarkoituksena oli ensinnäkin kehittää menetelmä fotogrammetristen sensorien epäsuoraan radiometriseen kalibrointiin ja arviointiin (Cal/Val) testikentällä. Toiseksi tutkittiin kolmen eri radiometrisen korjausmenetelmän soveltuvuutta fotogrammetrisille ilmakuville. Kolmanneksi tutkittiin auringon korkeuskulman vaikutusta ilmakuvien radiometriaan ja siten fotogrammetristen prosessien suorituskykyyn. Kehitetty radiometrinen Cal/Val menetelmä hyödyntää laboratoriossa tarkassa havaintogeometriassa tehtyjä heijastusmittauksia, jotka muunnetaan vastaamaan kuvausaikaisia sääolosuhteita maastossa tehtyjen referenssikohteiden nadiiriheijastusmittauksilla. Työssä tutkituilla radiometrisen korjauksen menetelmillä pystyttiin saavuttamaan 5 % heijastustarkkuus, kun käytettiin tarkkoja referenssikohteita. Muita kohteita käyttäen oli mahdollista saavuttaa 5-20 % heijastustarkkuus. Tulokset osoittivat myös, että 25° auringonkulma ei vaikuttanut fotogrammetristen prosessien suorituskykyyn eikä kolmiulotteisten pistepilvien luomiseen. Tässä työssä esitetty radiometrinen Cal/Val menetelmä on askel kohti fotogrammetrisen sensorien jäljitettävää kuvienkäsittelyketjua. Tulokset vahvistivat sensorien hyvät radiometriset ominaisuudet sekä todistivat niiden kuvien soveltuvan radiometriseen korjaukseen. Tämä mahdollistaa ilmakuvien radiometrian kvantitatiivisen käsittelyn sekä lisää automaattisten kuvantulkintamenetelmien tarkkuutta

Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

This is the final version. Available from MDPI via the DOI in this record. Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.European Space Agency (ESA)European Union’s Horizon 202

The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Airborne laser sensors and integrated systems

The underlying principles and technologies enabling the design and operation of airborne laser sensors are introduced and a detailed review of state-of-the-art avionic systems for civil and military applications is presented. Airborne lasers including Light Detection and Ranging (LIDAR), Laser Range Finders (LRF), and Laser Weapon Systems (LWS) are extensively used today and new promising technologies are being explored. Most laser systems are active devices that operate in a manner very similar to microwave radars but at much higher frequencies (e.g., LIDAR and LRF). Other devices (e.g., laser target designators and beam-riders) are used to precisely direct Laser Guided Weapons (LGW) against ground targets. The integration of both functions is often encountered in modern military avionics navigation-attack systems. The beneficial effects of airborne lasers including the use of smaller components and remarkable angular resolution have resulted in a host of manned and unmanned aircraft applications. On the other hand, laser sensors performance are much more sensitive to the vagaries of the atmosphere and are thus generally restricted to shorter ranges than microwave systems. Hence it is of paramount importance to analyse the performance of laser sensors and systems in various weather and environmental conditions. Additionally, it is important to define airborne laser safety criteria, since several systems currently in service operate in the near infrared with considerable risk for the naked human eye. Therefore, appropriate methods for predicting and evaluating the performance of infrared laser sensors/systems are presented, taking into account laser safety issues. For aircraft experimental activities with laser systems, it is essential to define test requirements taking into account the specific conditions for operational employment of the systems in the intended scenarios and to verify the performance in realistic environments at the test ranges. To support the development of such requirements, useful guidelines are provided for test and evaluation of airborne laser systems including laboratory, ground and flight test activities

Modélisation 3D du transfert raidatif pour simuler les images et données de spectroradiomètres et Lidars satellites et aéroportés de couverts végétaux et urbains

Les mesures de télédétection (MT) dépendent de l'interaction du rayonnement avec les paysages terrestres et l'atmosphère ainsi que des configurations instrumentales (bande spectrale, résolution spatiale, champ de vue: FOV,...) et expérimentales (structure et propriétés optiques du paysage et atmosphère,...). L'évolution rapide des techniques de télédétection requiert des outils appropriés pour valider leurs principes et améliorer l'emploi des MT. Les modèles de transfert radiatif (RTM) simulent des quantités (fonctions de distribution de la réflectance (BRDF) et température (BTDF), forme d'onde LiDAR, etc.) plus ou moins proches des MT. Ils constituent l'outil de référence pour simuler les MT, pour diverses applications : préparation et validation des systèmes d'observation, inversion de MT,... DART (Discrete Anisotropic Radiative Transfer) est reconnu comme le RTM le plus complet et efficace. J'ai encore nettement amélioré son réalisme via les travaux de modélisation indiqués ci-dessous. 1. Discrétisation de l'espace des directions de propagation des rayons. DART simule la propagation des rayons dans les paysages terrestres et l'atmosphère selon des directions discrètes. Les méthodes classiques définissent mal le centroïde et forme des angles solides de ces directions, si bien que le principe de conservation de l'énergie n'est pas vérifié et que l'obtention de résultats précis exige un grand nombre de directions. Pour résoudre ce problème, j'ai conçu une méthode originale qui crée des directions discrètes de formes définies. 2. Simulation d'images de spectroradiomètre avec FOV fini (caméra, pushbroom,...). Les RTMs sont de type "pixel" ou "image". Un modèle "pixel" calcule une quantité unique (BRDF, BTDF) de toute la scène simulée via sa description globale (indice foliaire, fraction d'ombre,...). Un modèle "image" donne une distribution spatiale de quantités (BRDF,...) par projection orthographique des rayons sur un plan image. Tous les RTMs supposent une acquisition monodirectionnelle (FOV nul), ce qui peut être très imprécis. Pour pouvoir simuler des capteurs à FOV fini (caméra, pushbroom,...), j'ai conçu un modèle original de suivi de rayons convergents avec projection perspective. 3. Simulation de données LiDAR. Beaucoup de RTMs simulent le signal LiDAR de manière rapide mais imprécise (paysage très simplifié, pas de diffusions multiples,...) ou de manière précis mais avec de très grands temps de calcul (e.g., modèles Monte-Carlo: MC). DART emploie une méthode "quasi-MC" originale, à la fois précise et rapide, adaptée à toute configuration instrumentale (altitude de la plateforme, attitude du LiDAR, taille de l'empreinte,...). Les acquisitions multi-impulsions LiDAR (satellite, avion, terrestre) sont simulées pour toute configuration (position du LiDAR, trajectoire de la plateforme,...). Elles sont converties dans un format industriel pour être traitées par des logiciels dédiés. Un post-traitement convertit les formes d'onde LiDAR simulées en données LiDAR de comptage de photons. 4. Bruit solaire et fusion de données LiDAR et d'images de spectroradiomètre. DART peut combiner des simulations de LiDAR multi-impulsions et d'image de spectro-radiomètre (capteur hyperspectral,...). C'est une configuration à 2 sources (soleil, laser LiDAR) et 1 capteur (télescope du LiDAR). Les régions mesurées par le LiDAR, dans le plan image du sol, sont segmentées dans l'image du spectro-radiomètre, elle aussi projetée sur le plan image du sol. Deux applications sont présentées : bruit solaire dans le signal LiDAR, et fusion de données LiDAR et d'images de spectro-radiomètre. Des configurations d'acquisition (trajectoire de plateforme, angle de vue par pixel du spectro-radiomètre et par impulsion LiDAR) peuvent être importées pour encore améliorer le réalisme des MT simulées, De plus, j'ai introduit la parallélisation multi-thread, ce qui accélère beaucoup les calculsRemote Sensing (RS) data depend on radiation interaction in Earth landscapes and atmosphere, and also on instrumental (spectral band, spatial resolution, field of view (FOV),...) and experimental (landscape/atmosphere architecture and optical properties,...) conditions. Fast developments in RS techniques require appropriate tools for validating their working principles and improving RS operational use. Radiative Transfer Models (RTM) simulate quantities (bidirectional reflectance; BRDF, directional brightness temperature: BTDF, LiDAR waveform...) that aim to approximate actual RS data. Hence, they are celebrated tools to simulate RS data for many applications: preparation and validation of RS systems, inversion of RS data... Discrete Anisotropic Radiative Transfer (DART) model is recognized as the most complete and efficient RTM. During my PhD work, I further improved its modeling in terms of accuracy and functionalities through the modeling work mentioned below. 1. Discretizing the space of radiation propagation directions.DART simulates radiation propagation along a finite number of directions in Earth/atmosphere scenes. Classical methods do not define accurately the solid angle centroids and geometric shapes of these directions, which results in non-conservative energy or imprecise modeling if few directions are used. I solved this problem by developing a novel method that creates discrete directions with well-defined shapes. 2. Simulating images of spectroradiometers with finite FOV.Existing RTMs are pixel- or image-level models. Pixel-level models use abstract landscape (scene) description (leaf area index, overall fraction of shadows,...) to calculate quantities (BRDF, BTDF,...) for the whole scene. Image-level models generate scene radiance, BRDF or BTDF images, with orthographic projection of rays that exit the scene onto an image plane. All models neglect the multi-directional acquisition in the sensor finite FOV, which is unrealistic. Hence, I implemented a sensor-level model, called converging tracking and perspective projection (CTPP), to simulate camera and cross-track sensor images, by coupling DART with classical perspective and parallel-perspective projection. 3. Simulating LiDAR data.Many RTMs simulate LiDAR waveform, but results are inaccurate (abstract scene description, account of first-order scattering only...) or require tremendous computation time for obtaining accurate results (e.g., Monte-Carlo (MC) models). With a novel quasi-MC method, DART can provide accurate results with fast processing speed, for any instrumental configuration (platform altitude, LiDAR orientation, footprint size...). It simulates satellite, airborne and terrestrial multi-pulse laser data for realistic configurations (LiDAR position, platform trajectory, scan angle range...). These data can be converted into industrial LiDAR format for being processed by LiDAR processing software. A post-processing method converts LiDAR waveform into photon counting LiDAR data, through modeling single photon detector acquisition. 4. In-flight Fusion of LiDAR and imaging spectroscopy.DART can combine multi-pulse LiDAR and cross-track imaging spectroscopy (hyperspectral sensor...). It is a 2 sources (sun, LiDAR laser) and 1 sensor (LiDAR telescope) system. First, a LiDAR multi-pulse acquisition and a sun-induced spectro-radiometer radiance image are simulated. Then, the LiDAR FOV regions projected onto the ground image plane are segmented in the spectro-radiometer image, which is also projected on the ground image plane. I applied it to simulate solar noise in LiDAR signal, and to the fusion of LiDAR data and spectro-radiometer images. To further improve accuracy when simulating actual LiDAR and spectro-radiometer, DART can also import actual acquisition configuration (platform trajectory, view angle per spectro-radiometer pixel / LiDAR pulse). Moreover, I introduced multi-thread parallelization, which greatly accelerates DART simulation

Satellite estimation of biophysical parameters for ecological models.

Ecological models are central to understanding of hydrological and carbon cycles. These models need input from Earth Observation data to function at regional to global scales. Requirements of these models and the satellite missions designed to fulfill them are reviewed to asses the present situation. The aim is to establish a better informed framework for the design and development of future satellite missions to meet the needs of ecological modellers. Key land surface parameters that can potentially be derived by remote sensing are analysed - leaf area index, leaf chlorophyll content, the fraction of photosynthetically-active radiation absorbed by the canopy and the fractional cover - as well as the aerosol optical thickness. Three coupled models - PROSPECT, FLIGHT and 6S - are used to simulate top of the atmosphere reflectances observed in a number of viewing directions and spectral wavebands within the visible and near-infrared domains. A preliminary study provides a sensitivity analysis of the top of the atmosphere reflectances to the input parameters and to the viewing angles. Finally, a methodology that links ecological model requirements to satellite instrument capabilities is presented. The three coupled models - PROSPECT, FLIGHT and 6S - are inverted using a simple technique based on look-up tables (LUTs). The LUT is used to estimate canopy biophysical variables from remotely-sensed data observed at the top of the atmosphere with different directional and spectral sampling configurations. The retrieval uncertainty is linked with the instrument radiometric accuracy by analysing the impact of different levels of radiometric noise at the input. The parameters retrieved in the inversion are used to drive two land-surface parameterization models, Biome-BGC and JULES. The effects of different configurations and of the radiometric noise on the NPP estimated are analysed. The technique is applied to evaluate desirable sensor characteristics for driving models of boreal forest productivity. The results are discussed in view of the definition of future satellites and the selection of the best measurement configuration for accurate estimation of canopy characteristics